- Europe avoided the hype of US and Japanese “AI winters” in the latter half of the 20th century by favouring cautious, engineering-style research.
- UK-based start-up DeepMind, which tries to create general-purpose deep-learning algorithms, made history by defeating South Korean grandmaster Lee Sedol 4-1 in the complex strategic game of Go.
Artificial-intelligence applications may be booming in the US and Asia, but AI’s roots are in Europe. Well before the 1956 Dartmouth College conference that gave the field its name, computing pioneers like Cambridge’s Alan Turing and Berlin’s Konrad Zuse made strong contributions to AI, and cybernetics pioneer Norbert Wiener battled Torres Quevedo’s 1912 chess machine at the 1951 Calculating Machines and Human Thought conference in Paris.
Wolfgang Wahlster, director and CEO of the German Research Centre for Artificial Intelligence, the world’s largest AI research centre, says Europe avoided the hype of US and Japanese “AI Winters” in the latter half of the century by favouring cautious, engineering-style research. The result was the development of many important AI fields – like Alexey Ivakhnenko’s foundation of deep learning in Ukraine in the 1960s – the building blocks of today’s rapid progress.
That fundamental research remains a European strength, according to Jürgen Schmidhuber, scientific director at the Dalle Molle Institute for Artificial Intelligence (IDSIA) in Switzerland, himself a pioneer in neural networks and machine learning. “Many, if not most of the basic breakthroughs in AI stem from Europe, even if that won’t be obvious to you if your only sources of info are blogs from Silicon Valley,” says Schmidhuber. “The Bay Area companies are heavily using fundamental AI methods developed by Europeans.” Schmidhuber says this is something being quickly recognised by major US and Asian companies, many of whom are outsourcing research to European labs and acquiring promising AI companies. The fact that this year Google has opened an AI research centre in Zurich – and Facebook one in Paris in 2015 – supports this observation.
UK-based start-up DeepMind, which tries to create general purpose deep learning algorithms, is a notable €460 million acquisition. One of their systems this year made headlines and history by defeating South Korean grandmaster Lee Sedol 4-1 in the complex strategic game of Go. Co-founded by IDSIA alumnus Shane Legg, DeepMind is also taking on real-world challenges, from partnering with the UK National Health Service to combating medical error to boosting Google server power efficiency, already reducing its data centre cooling bill by 40%.
Wahlster readily admits that European AI has its weaknesses – a lack of the consumer market applications and access to single-language markets present on the Pacific Rim – but he argues that trying to compete directly in these areas would be a mistake. He says Europe’s AI strength lies instead in its distribution, with many strong institutes as far apart as Barcelona, Trento, Edinburgh and Linköping. The language-fragmented European market even helped bring about today’s language technologies based on AI. “The EU funded this, and we’ve had a lot of success in machine translation, text understanding and spoken dialogue systems. With so many languages in Europe there is really demand, whereas in the US most people speak English or Spanish.”
Indeed, the original technology used to form the basis of Google Translate came from the Wahlster-led Verbmobil project, while the Long Short Term Memory neural network developed by Schmidhuber’s team enables applications like Google’s smartphone speech recognition. Wahlster says Europe also excels in the application of industrial AI in small companies. “We brought AI to the shop floor, so to speak, and we have a lot of smart factories that are full of AI features in the German Industrie 4.0 program,” he explains. Among examples he cites the development of collaborative co-bots working in teams with humans, as well as efficiency-boosted production lines combining internet-of-things sensors with machine learning algorithms, making mass customisation possible.
With such a groundwork of research and successful applications, Schmidhuber sees Europe moving from machine learning towards the first true, albeit basic AI in years, not decades. “In the not-so-distant future we’ll have a pretty impressive general purpose problem solver in little robots who can hear and see and act and do reasonable things in their environments, and learn by interacting with humans and through their own curiosity.”
Tax the machines
In the near future, more than half of all workers could be replaced by machines, say several studies. The financial consequences – notably the loss of tax income coupled with higher costs of social services –could be compensated by a tax on machines. Various European stakeholders– in the European Commission, in France and in Germany – are currently examining this problem.